Image segmentation is a branch of digital image processing that performs the task of categorizing, or classifying, the elements of a digital image into one or more class types. For medical imaging applications, it is common that image segmentation is performed on the voxel (volume element) of a 3-dimensional image data set with the classification types related to anatomical structure. In thoracic medical images, it is convenient to segment the image voxels into classes such as bone, lung parenchyma, soft tissue, bronchial vessels, blood vessels, etc. There are many reasons to perform such a task, such as surgical planning, treatment progress, and patient diagnosis.
Of interest is the image segmentation technology that allows a user of a Picture Archiving and Communications System (PACS) to segment a suspected cancerous pulmonary lesion. Starting with a seed point, i.e., a voxel position that is known to be part of a lesion, a region of contiguous voxels is grown, or developed, about the seed point. For such lesion segmentation algorithms, the only voxel value know for certainty that is characteristic of the lesion to be segmented is the seed point voxel. Thus, the statistical properties of the voxels associated with lesion to be segmented, such as the mean voxel value and the range of voxel values, must either be assumed a priori from experience or approximated.
Pulmonary lesions often grow adjacent to vessels (arteries, veins, or airways). The morphology of vessels and pulmonary lesions can be similar. Further complicating the geometry is the fact that cancerous and benign lesions can grow fully around a vessel. As a consequence, image segmentation algorithms often misclassify voxels in the vicinity of the junction between lesion and vessel tissue. The misclassification of voxels can also be the result of the uncertainties in the underlying statistics regarding the both the lesion and vessel tissues.
The results of three dimensional (3D) image segmentation processing is typically visualized with a computer graphics 3D routine that shows the set of segmented voxels (segmentation map) as a 3D object from a single point perspective shaded by a light source. This is usually accomplished by calculating a mesh model of connected points corresponding to voxels that are on the surface, or exterior, of the segmentation map. Alternatively, the volume rendering techniques can also be used to visualize the 3-dimensionality of the segmentation map.
Interactive 3D editing tools have been developed for the computer graphics industry and the medical imaging industry. U.S. Pat. No. 6,542,153 to Liu et al. discloses a method for constructing clipping planes used to modify segmented voxel image data by projecting vertices of a region of interest (ROI) in one plane and transforming the data within the ROI to allow all of a plurality of slices on the inside of the ROI to be along one axis of a three axis coordinate system. The method disclosed by Liu requires representing the inside of the ROI as a plurality of line segments, wherein only two coordinates and the length of a line segment are stored. The system is computationally intensive and complex for use in modifying the segmentation maps for pulmonary lesions.
The segmentation map resulting from the image segmentation processing is also viewed in the form of sequential slices. Undesirable structures of segmentation maps are most reliably removed using prior art manual editing methods. These methods typically have the user of the PACS manually draw outlines of the structures to be removed on every image slice of the segmentation map using careful hand-directed cursor manipulations. A disadvantage of such methods is that manual editing is a very repetitive, tedious, and time consuming process. When the number of image slices to be edited is large, as in a typical study to be 3D reconstructed using CT imaging, manual editing consumes expensive machine and operator time, notwithstanding that the operator is an expert.
Interactive 3D editing tools (such as 3D-Doctor found at www.3d-doctor.com) have also been developed for modifying medical images. Such tools allow the user to modify a segmentation map in a “cut mode.” The user clicks and drags with the mouse to form a cut line and then uses a parameter dialog box to determine how many slices of the segmentation map will be affected by the cut line. Although precise control can be exercised with such a tool, there are many manual operations that need to be performed to achieve an arbitrary planar cut.
Thus, a need exists for an interactive 3D editor that requires very little input to modify segmentation maps for pulmonary lesions.